experiment_fullRun.py

mainExperimentFolder = "VALID_PATH_NEEDED_HERE"

import MultiCGPTester
import GymFitnessFunctions
import FitnessCollapseFunctions
import TrainingOptimizers
import functionLists
import warnings
warnings.filterwarnings("ignore")

def main():
    doNothing = TrainingOptimizers.doNothingOptimizer()

    envs = ["CartPole-v1",
            "LunarLander-v2",
            "MountainCar-v0_modifiedReward",
            # "LunarLanderContinuous-v2",
            # "MountainCarContinuous-v0_modifiedReward"
            ]

    argList = []
    for env in envs:
        tempAct, tempObs = GymFitnessFunctions.getActionObservationSizes(env)
        argList.append(
          {
          'type': ['FFCGPANN'],
          'inputSize': [tempObs],
          'outputSize': [tempAct],
          'shape__rowCount': [1],
          'shape__colCount': [300],
          'shape__maxColForward': [-1],
          'shape__maxColBack': [301],
          'inputMemory': [None],
          'fitnessFunction': [GymFitnessFunctions.getEnvironmentTestFunction_tuple(env)],
          'functionList': [functionLists.funcListANN_singleTan],
          'populationSize': [7],
          'numberParents': [1],
          'numThreads': [1],  # Leave as 1. Environment testing is already multithreaded.
          'maxEpochs': [20000],
          'epochModOutput': [25],
          'bestFitness': [GymFitnessFunctions.getMaxScore(env)],
          'pRange': [[-1.0, 1.0]],
          'constraintRange': [[-1.0, 1.0]],
          'trainingOptimizer': [doNothing],
          'fitnessCollapseFunction': [FitnessCollapseFunctions.minOfMeanMedian],
          'completeFitnessCollapseFunction': [None],
          'mutationStrategy': [{'name': 'activeGene', 'numGenes': [1, 1]}],
          # We're experimenting on different arity values and ranges. Put in all
          # varieties:
          'vsp__inputsPerNeuron': [[2, 2], [3, 3], [4, 4], [5, 5], [6, 6],
                                   [7, 7], [8, 8], [9, 9], [2, 3], [2, 4],
                                   [2, 5], [2, 6], [2, 7], [2, 8], [2, 9],
                                   [3, 4], [3, 5], [3, 6], [3, 7], [3, 8],
                                   [3, 9], [4, 5], [4, 6], [4, 7], [4, 8],
                                   [4, 9], [5, 6], [5, 7], [5, 8], [5, 9],
                                   [6, 7], [6, 8], [6, 9], [7, 8], [7, 9],
                                   [8, 9]],
          'vsp__weightRange': [[-1.0, 1.0]],
          'vsp__switchValues': [[1, 1]]
          }
        )

    # Run each environment as its own MultiCGPTester so that if we hit an error
    # or processing is stopped for any reason we have already saved off all
    # results from previous environments:
    for expTup in zip(envs, argList):
        experimentFolder = "%s/%s" % (mainExperimentFolder, expTup[0])

        tester = MultiCGPTester.MultiCGPTester(
          expTup[1],
          runsPerVariation=1,
          periodicModelSaving=500,
          experimentFolder=experimentFolder)

        _ = tester.runTests(None, None, confirmPrompt=True)


if __name__ == '__main__':
    main()